One of the main limitations of PDPs is that they bear the risk of providing misleading results if applied to correlated data in the presence of interactions, especially for nonparametric models [1]}.
However, existing alternatives that visualize the global marginal effect of a feature such as accumulated local effect (ALE) plots [2]} do also not provide a fully satisfying solution to this problem [1]}. As a solution to this problem,
[1]} suggests stratified PDPs by conditioning on a correlated and potentially interacting feature to group ICE curves.
This idea is in the spirit of our introduced tree-based partitioning algorithm.
However, in the context of BO we might assume the distribution in Eq. (REF ) to be uniform and therefore no correlations are present.
Instead of correlated features, we are faced with a sampling bias (see Section ) where we observe regions of varying uncertainty.
Hence, instead of stratifying with respect to correlated features and aggregating ICE curves in regions with less correlated features, we stratify with respect to uncertainty and aggregate ICE curves in regions with low uncertainty variation.
Nonetheless, it might be interesting to compare our approach with approaches based on the considerations made by [1]} – or potentially improved ALE curves.
